Design-Based Methods for Ranking Questions

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Documentation for package ‘rankingQ’ version 0.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

add_ipw_weights	Add IPW Weights to the Original Data
autoplot.rankingQ_output	Plot rankingQ estimator outputs
avg_rank	Compute the Average Rank of All Items
identity	Identity-ranking data analyzed in Atsusaka and Kim (2025)
identity_w	Identity-ranking data with estimated weights based on inverse probability weighting
imprr_direct	Implements Plug-in Bias-Corrected Estimators for Ranking Data
imprr_direct_rcpp	Implements Plug-in Bias-Corrected Estimators for Ranking Data (Rcpp)
imprr_weights	Computes Bias-Correction Weights for Ranking Data
imprr_weights_boot	Bootstrap IPW-Based Bias-Corrected Estimates for Ranking Data
item_to_rank	Return Rankings with Items as Columns
ordinal_seq	Generate an Ordinal Sequence from a Number
permn_augment	Augmenting Permutation Patterns
plot.rankingQ_output	Plot rankingQ estimator outputs
plot_avg_ranking	Plot Average Rank Results
plot_dist_ranking	Plot the Distribution of Rankings Over the Permutation Space
print.summary.rankingQ_output	Summarize rankingQ estimator outputs
rank_longer	Convert Ranking Columns from Wide to Long Format
rank_wider	Turn Long Ranking Data into a Wide Format
recover_recorded_responses	Recover the Recorded Responses Given that Ranking Items were Randomized
rpluce	Draw Samples from the Plackett-Luce Model
stratified_avg	Stratified Estimate of Average Ranks
summary.rankingQ_output	Summarize rankingQ estimator outputs
table_to_tibble	Turn the Frequency Table into a Tibble or Data Frame
tidy.rankingQ_output	Tidy rankingQ estimator outputs
unbiased_correct_prop	Unbiased Estimator of the Proportion of Random and Non-random Responses
uniformity_test	Uniformity Test for Ranking Patterns